System and method for satellite positioning

ABSTRACT

A method and system for determining a receiver position comprising receiving satellite observations from a set of satellites, determining differenced observations based on the satellite observations, determining an all-in-view position of the receiver based on the differenced observations, determining a set of fault modes each associated with a subset of the differenced observations, for a fault mode of the set of fault modes, determining a fault-tolerant position of the receiver using the subset of differenced observations associated with the fault mode, when the all-in-view position and the fault tolerant position of the receiver for each fault mode are within a solution separation threshold, calculating a protection level associated with the all-in-view position of the receiver.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/036,748, filed 9 Jun. 2020, which is incorporated in its entirety bythis reference.

TECHNICAL FIELD

This invention relates generally to the satellite positioning field, andmore specifically to a new and useful system and method in the satellitepositioning field.

BACKGROUND

Traditional receiver autonomous integrity monitoring algorithms (RAIMsuch as advanced RAIM (ARAIM), extended RAIM (ERAIM), relative RAIM(RRAIM), etc.) are designed to estimate an integrity of a receiverposition determined using satellite observations. However, thesetechniques typically estimate the integrity using satellite pseudorange,limiting the position accuracy of the solution and providing largeprotection levels such as on the order of meters to tens of meters.Emerging problems in receiver positioning can benefit from improvedaccuracies. Thus, there is a need in the satellite positioning field tocreate a new and useful system and method. This invention provides suchnew and useful systems and methods.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of the system.

FIG. 2 is a schematic representation of the method.

FIG. 3 is a schematic representation of an embodiment of determiningfault modes for the satellite observations and determining a faultparameter in the satellite observations.

FIG. 4 is a schematic representation of an embodiment of transformingthe satellite observations.

FIG. 5A is a schematic representation of an example of determining thefault modes.

FIG. 5B is a schematic representation of an example of determining thefault modes where a subset of the fault modes correspond to transformedsatellite observations using a different reference satellite from thesatellite observations.

FIG. 6 is a schematic representation of an example of the method.

FIG. 7 is a schematic representation of an example of a solutionseparation threshold excluding and including solution covariances in twodimensions.

FIGS. 8A and 8B are schematic representations of exemplary receiverposition and protection level determination directions.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview.

As shown in FIG. 2, the method can include receiving satelliteobservations S100, determining fault modes for the satelliteobservations S400, determining a fault parameter S500, and determiningposition and protection levels S700. The method can optionally includeprocessing the satellite observations S200, determining the carrierphase ambiguity for the satellite observations S300, mitigating aneffect of a fault S600, and/or any suitable steps.

As shown in FIG. 1, the system 10 can include a GNSS receiver 100 and acomputing system 200. The system can optionally include one or morereference station 300. The GNSS receiver and the reference station arepreferably coupled to a set of satellites (e.g., configured to receivesignals from each satellite of the set of satellites). The set ofsatellites can correspond to one or more satellite constellation (e.g.,GPS, BDS, Galileo, GLONASS, etc.).

The system and method preferably function to estimate a position andassociated protection levels of a GNSS receiver. Embodiments of thesystem and/or method can be used, for example, in autonomous orsemi-autonomous vehicle guidance (e.g., for unmanned aerial vehicles(UAVs), unmanned aerial systems (UAS), self-driving cars, agriculturalequipment, robotics, rail transport/transit systems, autonomoustrucking, last mile delivery, etc.), GPS/GNSS research, surveyingsystems, user devices, mobile applications, internet-of-things (IOT)devices, and/or may be used in any other suitable application. Inspecific examples, the system (and/or components) can be coupled to anysuitable external system such as a vehicle (e.g., UAV, UAS, car, truck,etc.), robot, railcar, user device (e.g., cell phone), and/or anysuitable system, and can provide positioning data, integrity data (e.g.,protection level data), and/or other data to said system.

2. Benefits.

Variations of the technology can confer several benefits and/oradvantages.

First, variants of the technology can estimate protection levels and/orreceive position using only carrier phase (e.g., differenced carrierphase), which can enable the technology to better integrate withexisting real-time kinematics (RTK) GNSS solutions. By using carrierphase, these variants can enable higher accuracy position determinationand/or tighter tolerances for the protection levels. In a specificexample, using carrier phase can be enabled by transforming thesatellite observations (e.g., to differenced satellite observations)and/or by considering fault modes that account for differenced satelliteobservations.

Differenced satellite observations are generally incompatible withconventional RAIM methods because the observations are now correlatedvia the reference satellite used for differencing, while RAIM (e.g.,ARAIM) assumes uncorrelated observations. The inventors have discoveredthat, when analyzing the (differencing) reference satellite as the faultmode, a different reference satellite can be used without substantialloss of accuracy and/or precision, such that fault mode detection can beused with correlated observations. In particular, the inventors havefound that no matter which reference satellite is chosen for a givenfault mode, a legitimate protection level (e.g., a protection level suchthat the probability of an error in the position estimate exceeding theprotection level is guaranteed to be less than a given threshold) willresult. Relatedly, the inventors have found that any reference satellite(e.g., for each fault mode) can be selected from the set of non-faultingsatellites and the resulting protection level will be legitimate. Theinventors have also modified the fault-tolerant position calculationsand the solution separation test to accommodate for the differencedobservations.

Second, variants of the technology can enable more accurate (e.g.,conservative, tighter, more representative, etc., in one or morecoordinates) bounds on the protection level to be achieved (e.g., ascompared to traditional RAIM technology). More accurate bounds on theprotection level can be beneficial for ensuring or providing moreaccurate knowledge of the probable receiver position. In a specificexample, the more accurate bounds can be achieved by accounting for thecovariances and/or correlations between satellite observations and/orreceiver positions in the solution separation threshold.

Third, variants of the technology can determine an accurate (e.g.,accurate to within 0.01%, 0.1%, 1%, 2%, 5%, 10%, 20%, 25%, 50%, etc. ofthe actual) upper bound on the error of (e.g., a protection level of) areceiver position estimate (e.g., a high accuracy receiver positionestimate). These variants preferably achieve said protection level usinga low CPU load. In specific examples, accurate protection levels can beachieved by correcting for correlations or covariances in satelliteobservations, storing intermediate data between receiver positionestimates, and/or by generating optimal (e.g., including the maximalnumber of fault tolerant satellite observations) fault modes.

However, variants of the technology can confer any other suitablebenefits and/or advantages.

3. System.

The system 10 preferably functions to determine the position andassociated integrity of a receiver.

The system 10 preferably uses a set of data collected by one or moredata sources. Data sources can include: receivers (e.g., GNSS receivers,antennae, etc.), sensors (e.g., located onboard the receiver, theexternal system, the reference stations, etc.), databases, satellites,reference stations, and/or any other suitable data source. Examples ofdata that can be used include: satellite observations, sensorobservations, and/or any other suitable data.

The receiver 100 (e.g., GNSS receiver, mobile receiver, antenna, etc.)preferably functions to receive a set of satellite observations (e.g.,satellite signals such as carrier phase and satellite code) from one ormore satellites. In variants, the receiver (e.g., a processor of areceiver) can determine the location of the receiver (and/or externalsystem) based on the satellite observations. The receiver is preferablyin communication with the computing system. However, the receiver can beintegrated with the computing system, and/or the receiver and computingsystem can be arranged in any suitable manner. The receiver ispreferably a stand-alone device (e.g., a GNSS receiver, antenna).However, the receiver can be integrated into the external system (e.g.,be a component of an automobile, aero vehicle, nautical vehicle, mobiledevice, etc.), can be a user device (e.g., smart phone, laptop, cellphone, smart watch, etc.), and/or can be configured in any suitablemanner.

The set of satellite observations can include orbital data, timestamp,code data, carrier phase data, pseudocode data, and/or any suitabledata. The set of satellite observations can be associated with metadata(e.g., ephemeris), and/or any suitable data. The set of satelliteobservations preferably includes satellite observations corresponding tosatellites from a plurality of satellite constellations (e.g., GlobalPositioning System (GPS), GLObal Navigation Satellite System (GLONASS),BeiDou navigation satellite System (BDS), Galileo, etc.). However, theset of satellite observations can correspond to satellites from a singlesatellite constellation, can include data from an augmentation system(e.g., Satellite Based Augmentation System (SBAS) such as Wide AreaAugmentation System (WAAS), European Geostationary Navigation OverlayService (EGNOS), Multi-Functional Satellite Augmentation System (MSAS),Omnistar, StarFire, etc.; Ground Based Augmentation Systems (GBAS) suchas Local Area Augmentation System (LAAS); etc.), and/or can include anysuitable data. Each satellite observation from the set of satelliteobservations preferably corresponds to a common time window (e.g.,epoch). However, each satellite observation can be associated with atime stamp (e.g., time of transmission, time of receipt, time ofprocessing, etc.), different time windows (e.g., different epochs),and/or the satellite observations can have any suitable timing.

In variants of the system including more than one receiver, eachreceiver can be configured to receive satellite observationscorresponding to a satellite constellation, to a carrier frequency(e.g., the L1, L2, L5, E1, E5a, E5b, E5ab, E6, G1, G2, G3, B1, B2, B3,LEX, etc. frequencies), and/or corresponding to any suitable source.

The receiver can be in communication with a correction service (e.g., anetworked correction service, PPP correction service, PPP-RTK correctionservice, etc.), which can provide corrections (e.g., for globalcorrections such as clock, orbit, etc.; for local corrections such asionosphere delay, troposphere delay, etc.; etc. such as those asdisclosed in U.S. patent application Ser. No. 16/589,932 filed 1 Oct.2019 entitled “SYSTEMS AND METHODS FOR DISTRIBUTED DENSE NETWORKPROCESSING OF SATELLITE POSITIONING DATA” and/or U.S. patent applicationSer. No. 16/983,706 filed 3 Aug. 2020 entitled “SYSTEM AND METHOD FORGAUSSIAN PROCESS ENHANCED GNSS CORRECTIONS GENERATION,” each of which isincorporated in its entirety by this reference) for one or more of thesatellite observations.

In variants of the system including reference stations 300 (e.g.,reference receivers), the reference station(s) preferably function toreceive a set of satellite observations (e.g., reference stationsatellite observations, reference observations, etc.) and transmit thereference station satellite observations (e.g., to the computing system,to the receiver). The satellite observations from the referencestation(s) can be used to determine corrections (e.g., local and/orglobal corrections such as to account for atmospheric effects, toaccount for clock errors, etc.) to the set of satellite observationscorresponding to the receiver, can be used to transform the satelliteobservations (e.g., computing differenced satellite observations),and/or can otherwise be used. Each reference station is preferablycommunicably coupled to the computing system. However, the referencestation can include the computing system and/or be coupled to thecomputing system in any suitable manner. The reference stations can bein direct or indirect (e.g., via an intermediary) communication with thereceiver. The reference station(s) are preferably located within about500 km of the receivers, but the distance between the reference stationsand the receiver can be any suitable distance.

The reference station satellite observations preferably correspond tothe same set of satellites as the set of satellite observations.However, the reference station satellite observations can correspond toany suitable satellite observations.

The location (e.g., position) of the reference station(s) is preferablyknown to a high degree of accuracy (e.g., less than 1 mm, 1 cm, 1 dm, 1m, etc. of uncertainty in the location of the reference station such asin the location of the reference station antenna). The location of thereference station(s) can be static and/or dynamic.

The computing system 200 preferably functions to process the data (e.g.,satellite observations) from the receiver and/or the reference stations.The computing system can: aggregate the data (e.g., combine the receiversatellite observations, reference station satellite observations, andsensor data; reorganize the receiver satellite observations, referencestation satellite observations, and sensor data such as based on thetime stamp, time of transmission, time of receipt, etc.; etc.), filterthe data (e.g., to calculate state vectors, ambiguities such as phaseambiguities, etc. associated with the data), calculate the receiverposition (e.g., based on the ambiguities), calculate the protectionlevel, determine the fault modes, determine a fault parameter, correctthe data (e.g., correct the satellite observations for clock errors,hardware bias, atmospheric effects, etc.), and/or can process the datain any suitable manner. The computing system can be local (e.g.,on-board the external system, integrated in a receiver, integrated witha reference station, etc.), remote (e.g., cloud computing, server,networked, etc.), and/or distributed (e.g., between a remote and localcomputing system).

The computing system 200 is preferably configured to perform a RAIMcalculation (e.g., RAIM, ARAIM, ERAIM, RRAIM, modified RAIM, modifiedARAIM, modified ERAIM, modified RRAIM, CRAIM, PRAIM, HRAIM, VRAIM, etc.)to estimate the receiver position and protection level, but can estimatethe receiver position and protection level using any algorithm. Theinputs to the RAIM calculation are preferably the carrier phase (e.g.,carrier phase with ambiguities resolved) and/or the satellite code data,but can additionally or alternatively include pseudorange, ambiguitiesand/or any suitable data. In a specific example, the inputs to the RAIMcalculation are only the carrier phases associated with satellites ofthe set of satellites. In a variation of this specific example, theinputs to the RAIM calculation can be only carrier phases that havefixed and/or validated integer ambiguities. In a second variation ofthis specific example, only differenced satellite observations (forinstance only double differenced observations such as to obtain aprotection level associated with an RTK baseline positioning solution)can be used as inputs to the RAIM calculation. In a third variation,only differenced satellite observations that have fixed and/or validatedinteger ambiguities can be used as inputs to the RAIM calculation.However, any suitable inputs to the RAIM calculation can be used.Outputs from the RAIM calculation can include a receiver position, areceiver position integrity (e.g., integrity risk, protection level,etc.), fault information (e.g., fault identification, fault mitigation,fault detection, fault impact, etc.), and/or any suitable information.

The computing system is preferably communicably coupled to the receiver,to the reference station, and to the sensors, but the computing systemcan be in communication with any suitable components. In variants, thecomputing system can include one or more: processing module, deadreckoning module, and validation module. However, the computing systemcan include any suitable modules.

In specific examples, the system can include one or more components asdescribed in U.S. application Ser. No. 16/817,196 filed 12 Mar. 2020,and/or U.S. application Ser. No. 16/865,077 filed 1 May 2020, each ofwhich is incorporated herein its entirety by this reference, orotherwise configured.

4. Method.

The method preferably functions to estimate (e.g., calculate, determine)the position of the receiver and the associated integrity (e.g.,protection level). Steps and/or substeps of the method can be performediteratively (e.g., for different epochs, for the same epoch, etc.),sequentially, and/or in any suitable order. The steps and/or substeps ofthe method can be performed in series and/or in parallel. The stepsand/or substeps are preferably performed by a system as described above,but can be performed by any system. The integrity is preferably aconservative integrity (e.g., overestimate of the actual protectionlevel and/or receiver position, overestimate of the integrity risk,overestimate of the total integrity risk, etc. such as to better ensurethat the external system can be operated according to target operationparameters), but can produce an exact and/or aggressive integrity (e.g.,underestimate of the actual protection level). The method is preferablyperformed in real or near-real time (e.g., can be completed during asingle satellite observation epoch, completed before additionalsatellite observations associated with a satellite are received, etc.),but can be performed with a delay (e.g., lagging to accommodate a delayin one or more data source), offline, and/or with any suitable timing.

The method and/or steps thereof (particularly, but not exclusively,steps S400 through S700) can be performed using and/or be referred to asa modified ARAIM algorithm. For instance, the ARAIM algorithm can bemodified to process carrier phase observations (in addition to oralternative to pseudorange or code observations), modified to processdifferenced carrier phase observations, modified to processpredetermined sets or subsets of satellite constellations, modified toaccount for observation and/or positional covariances (in addition to oralterative to variances), and/or otherwise be modified. However, themethod and/or steps thereof can additionally or alternatively beperformed using any suitable RAIM algorithm(s) and/or any suitablealgorithm. The RAIM algorithm (e.g., modified ARAIM algorithm) can use ameasurement rejection approach (MRA), an error characterization approach(ECA), and/or any suitable approach. The RAIM algorithm (e.g., modifiedARAIM algorithm) can perform fault detection, fault exclusion, faultidentification, fault mitigation, and/or any suitable steps inprocessing or determining a receiver position.

The method can include receiving satellite observations S100,determining fault modes for the satellite observations S400, determininga fault parameter S500, and determining position and protection levelsS700. The method can optionally include processing the satelliteobservations S200, determining the carrier phase ambiguity for thesatellite observations S300, mitigating an effect of a fault S600,and/or any suitable steps.

Receiving the satellite observations S100 functions to measure and/ordetect a set of satellite signals, where each satellite signal isassociated with a satellite, at a GNSS receiver (e.g., a mobilereceiver) and/or reference station. Satellite observations correspondingto or associated with the same set of satellites are preferably receivedat the GNSS receiver and the reference station; however, the GNSSreceiver and the reference station can receive satellite observationsfrom any satellites. The satellite observations preferably refer tocarrier phase and/or satellite signal code data. In a first specificexample, the satellite observations can include only satellite carrierphase. In a second specific example, the satellite observations caninclude only satellite code data. In a third specific example, thesatellite observations can include both satellite carrier phase and codedata. However, the satellite observations can additionally oralternatively include satellite pseudorange, and/or any suitable data.Receiving the satellite observations can include transmitting thesatellite observations to the GNSS receiver and/or to the computingsystem.

S100 is preferably performed before processing the satelliteobservations S200; however, S100 can be performed at the same time asS200. S100 is preferably performed by a receiver. However, S100 canadditionally or alternatively be performed by one or more referencestations, by one or more sensors, and/or by any suitable component.

The data (e.g., satellite observations, sensor data, etc.) received inS100 is preferably measured during a time window (e.g., time period,epoch). Each of the sets of satellite observations can correspond to thesame and/or to a different time window. In variants, after the timewindow has expired or ended, the method can include repeating S100during a second time window. The second time window can be the sameduration as the time window and/or a different duration. The second timewindow preferably immediately follows the time window; however, thesecond time window can be delayed relative to the time window by anyamount. The (first and second) time windows are preferably separate anddistinct (e.g., consecutive, nonconsecutive, etc.), but canalternatively overlap or otherwise be related. The second time windowcan additionally or alternatively be started in response to a triggerevent (e.g., convergence of one or more calculation; receiver position;loss of satellite observations such as for a specific satellite, for anamount of time, etc.; etc.), at a predetermined time, and/or at anysuitable time. However, the time window can be extended and/or anysuitable action can occur in response to the trigger event.

Processing the satellite observations S200 preferably functions totransform the set of satellite observations, but can function to processthe set of satellite observations in any manner (e.g., to denoise theobservations, to mitigate an effect of outliers, etc.). Processing thesatellite observations is preferably performed by a computing system(e.g., a processing module of a computing system, by a processor of thedata receiver such as the mobile receiver, GNSS receiver, referencestation, etc.), but can be performed by any component. Processing thesatellite observations can include: removing non-carrier phaseobservations from the set of satellite observations, aligning thecarrier phase observations and the validated ambiguities (e.g., from aprevious epoch, from the current epoch, etc.), transforming the carrierphase observations to a differenced and/or disambiguated space,generating linear combinations of satellite observations, fixing thecarrier phase observations (e.g., using the integer phase ambiguities,the validated integer phase ambiguities, etc.), correcting the satelliteobservations (e.g., based on corrections data generated by or receivedfrom a corrections service), and/or any suitable step(s). S200 can beperformed before, during, and/or after S300.

The set of processed satellite observations can be correlated (e.g.,form correlated sets of satellite observations such as differencedsatellite observations), uncorrelated, and/or include both correlatedand uncorrelated satellite observations. Variants where the processedsatellite observations are correlated can be particularly beneficial forfacilitating fault mode determination and fault mode counting as inthese variants, the number of fault modes can correspond to a sum ofbinomial distributions (for instance based on the number and/or type ofprocessing). Variants where the processed satellite observations arecorrelated can be beneficial for facilitating fault mode isolation.However, the variants can facilitate fault mode generation and/ordetermination in any manner.

In some embodiments of the method (as shown for example in FIG. 8B), afirst set of processed satellite observations can be used to determinesatellite observation ambiguities and/or a receiver position and asecond set of processed satellite observations can be used to determinea receiver position and/or an integrity of a receiver position. Thefirst and second set of processed satellite observations are preferablyderived from the same satellite observations. However, either the firstor the second set of processed satellite observations can includeinformation derived associated with additional or fewer satellites orother data sources. The first and the second set of processed satelliteobservations can be processed in the same manner or in differentmanners. However, a single set of processed satellite observations canbe used (e.g., as shown for example in FIG. 8A), and/or any suitablenumber of processed satellite observations can be derived.

In specific examples (particularly of correlated satelliteobservations), the satellite observations can be transformed to a narrowlane, a wide lane, an extra wide lane, a Melbourne-Wübbena wide lane, aHatch-Melbourne-Wübbena combination, a geometry free linear combination,an ionosphere-free linear combination, and/or other linear combinationof satellite observations.

In specific examples, processed satellite observations can include(and/or correspond to) differenced satellite observations (e.g.,differenced space). The differenced satellite observations can include asingle difference (e.g., between satellite observations received at theGNSS receiver, between satellite observations received at the GNSSreceiver and a satellite observations received at a reference station,etc.), double difference (e.g., between satellite observations receivedat the GNSS receiver and the reference station, between satelliteobservations received at the GNSS receiver and different referencestations), triple differenced (e.g., between satellite observationsreceived during a first epoch at the GNSS receiver and the referencestation and satellite observations received during a second epoch at theGNSS receiver and the reference station), and/or any suitable differencecan be computed. A single reference satellite (e.g., per satelliteconstellation) is preferably used to calculate the differences (e.g.,each difference is computed relative to the same satellite). In anillustrative example, double-differenced satellite observations caninclude GNSS receiver and reference station sources, and can bedifferenced within a code type against a reference satellite. However,additionally or alternatively, a single reference satellite can be used,a plurality of reference satellites can be used, and/or any data can beused to compute the difference.

In a first illustrative example such as when the satellite observations(e.g., observation space) only include carrier phase, the transformationfrom the measured satellite observations to the wide-lane doubledifferenced space can be:∇Δ y=TH _(A) ⁻¹ y−A

where H_(a) transforms the satellite carrier phase (e.g., observationspace of) y (e.g., in units of length such as meters) to a phase space y(e.g., in units of cycles), T transforms the phase space y to a doubledifferenced phase space ∇Δy, and A are the carrier phase ambiguities(e.g., determined in S300, determined for a previous epoch, determinedfor the current epoch, etc.).

In a second illustrative example as shown in FIG. 4, the transformationfrom the measured satellite observations to the wide-lane doubledifference space can be:∇Δ y′=T′H′ _(A) ⁻¹ y′−A

Where y′=[_(φ) ^(ρ)], where ρ corresponds to noncarrier phaseobservations (e.g., satellite code, pseudorange, etc.), φ correspond tocarrier phase observations,

$T^{\prime} = \begin{bmatrix}I & 0 \\0 & T\end{bmatrix}$ and $H_{A}^{\prime} = {\begin{bmatrix}I & 0 \\0 & H_{A}\end{bmatrix}.}$

However, any transformation(s) can be used.

Determining the carrier phase ambiguities S300 functions to determine anunknown ambiguity in the carrier phase of each satellite observation.The unknown ambiguity is preferably determined to be an integerambiguity, but can be determined as a floating (e.g., floatingprecision) ambiguity and/or determined in any precision. S300 can beperformed before, during, and/or after S200. S300 is preferablyperformed by a computing system (e.g., a GNSS receiver computing system,a cloud computing system, etc.), but can be performed by any suitablecomponent. The carrier phase ambiguities are preferably validated, butcan be unvalidated. Determining and/or validating the carrier phaseambiguity can be performed as described in U.S. application Ser. No.16/817,196 filed 12 Mar. 2020 entitled “SYSTEMS AND METHODS FOR REALTIME KINEMATIC SATELLITE POSITIONING”, U.S. patent application Ser. No.16/685,927 filed 15 Nov. 2019 entitled “SYSTEM AND METHOD FOR SATELLITEPOSITIONING”, U.S. application Ser. No. 17/022,924 filed 16 Sep. 2020entitled “SYSTEMS AND METHODS FOR HIGH-INTEGRITY SATELLITE POSITIONING”,and/or U.S. application Ser. No. 17/235,333 filed 20 Apr. 2021 entitled“SYSTEM AND METHOD FOR VALIDATING GNSS AMBIGUITIES”, each of which isincorporated in its entirety by this reference, and/or otherwisedetermined or validated.

S300 can include: determining a set of floating phase ambiguityhypotheses, determining a set of integer phase ambiguity hypotheses fromthe set of floating phase ambiguity hypotheses, performing hypothesistesting to validate the set of integer phase ambiguities hypotheses,and/or any suitable steps.

Determining a set of floating phase ambiguity hypotheses preferablyfunctions to determine a phase ambiguity (typically in a floating pointrepresentation, but other representations can be used) associated witheach satellite represented in the set of satellite observations. The setof floating phase ambiguity hypotheses can be determined using snapshotmethods (e.g., using a snapshot least squares calculation) using afilter, and/or using any suitable method. The filter can be a Kalmanfilter, an extended Kalman filter, an unscented Kalman filter, aBierman-Thornton filter, a particle filter, a snapshot least-squaresfilter, a Monte Carlo simulation, and/or any suitable mean-square errorfilter and/or sensor fusion algorithm. The inputs to the floatinghypotheses determination method can include: satellite observations(e.g., receiver satellite observations, corrected satelliteobservations, reference station satellite observations, processedsatellite observations, etc. preferably associated with a single epochbut potentially associated with a plurality of epochs), sensor data,and/or any suitable data. The output of the floating hypothesesdetermination method is preferably one or more floating carrier phaseambiguity hypotheses, but can include any suitable states (e.g., statevector) or information.

Determining a set of integer phase ambiguity hypotheses preferablyfunctions to constrain (and/or otherwise determine from) the floatingphase ambiguity hypotheses to a set of integer phase ambiguityhypotheses. Integer phase ambiguities can be beneficial for improving anaccuracy of the determined receiver position as compared to the receiverposition determined using floating ambiguities. Determining the set ofinteger phase ambiguity hypotheses can include reducing a correlationbetween ambiguities of the set of phase ambiguities (e.g., floatingphase ambiguity hypotheses), performing a search (e.g., a least squaressearch, an absolute value search, etc.) to identify possible integerphase ambiguities to include in the set of integer phase ambiguityhypotheses, and/or include any suitable steps. For example,Least-Squares Ambiguity Decorrelation Adjustment (LAMBDA) algorithm,modified-LAMBDA (MLAMBDA) algorithm, LLL reduction algorithm, awhitening transformation, a coloring transformation, a decorrelationtransformation, rounding, the Ambiguity Function Method (AFM), FastAmbiguity Resolution Approach (FARA), Least-Squares Ambiguity SearchTechniques (LSAST), integer bootstrapping, and/or any suitabledecorrelation or reduction algorithm can be used to fix and/or identifyinteger phase ambiguities. The set of integer phase ambiguity hypothesespreferably includes integer phase ambiguities that satisfy a hypothesiscriterion, but can include any suitable integer phase ambiguities.Example of hypotheses criterion include a threshold sum of leastsquares, a threshold sum of absolute differences, a threshold likelihoodof the correct solution, and/or any suitable criterion. However, the setof integer phase ambiguity hypotheses can be generated in any manner.

Performing hypothesis testing preferably functions to determining theinteger phase ambiguities from the set of integer phase ambiguityhypotheses that are most likely to be correct. The hypothesis test ispreferably a Bayesian inference, but can additionally or alternativelybe based on a statistical confidence, significance testing, and/or anysuitable hypothesis testing. Examples of hypothesis tests can includedifference test, ratio test, projector test, f-test GIA test, and/or anysuitable hypothesis test. In an illustrative example, performinghypothesis testing can include determining a probability (e.g., alikelihood, a log likelihood, etc.) associated with subsets of integerphase ambiguities of the integer phase ambiguity hypotheses; computing aratio of the probability between two subsets of integer phaseambiguities (e.g., within the same ambiguity set, across differentambiguity sets); when the ratio between a most-likely and a next mostlikely subset of integer phase ambiguities exceeds a threshold, storingthe most-likely subset of integer phase ambiguities as the integer phaseambiguities and ceasing hypothesis testing. However, any hypothesistesting can be performed.

However, determining the carrier phase ambiguity can otherwise beperformed.

Determining fault mode(s) S400 preferably functions to determine (e.g.,identify, generate, list, enumerate, etc.) a set of fault modes for thesatellites and/or satellite observations. Each fault mode of the set offault modes can refer to (or be associated with) unique sets ofsatellite observations that could potentially fail, unique sets ofsatellites that could potentially fail, satellite constellation failure,each unique processed satellite observation that could potentially fail,and/or to any potential failure. As an illustrative example, if a set ofsatellite observations included three satellite observations, the set offault modes could include up to 6 fault modes: 3 single satellite faultmodes, 2 double satellite fault modes, and a triple satellite faultmode. Each fault mode can be associated with a set of excludedsatellites (e.g., individual satellites, constellations, constellationsubsets, etc.), a predicted probability of the fault occurring, and/orany suitable information. Determining the set of fault modes ispreferably performed by the computing system (e.g., a fault module ofthe computing system, of the GNSS receiver, of the external system, of acloud computing system, etc.), but can be performed by any component.Examples of faults can include: satellite failure, satelliteconstellation failure, non-line of sight measurements (e.g., multipath),clock errors, atmospheric effects, orbit errors, ephemeris errors,receiver faults, cycle slip, and/or any suitable faults (orpredetermined events such as those disclosed in U.S. application Ser.No. 17/022,924 filed 16 Sep. 2020 entitled “SYSTEMS AND METHODS FORHIGH-INTEGRITY SATELLITE POSITIONING” incorporated in its entirety bythis reference).

S400 can be performed by a computing system (e.g., of an externalsystem, of a GNSS receiver, cloud computing system, etc.) and/or by anysuitable component. S400 is preferably performed after S100, but can beperformed at the same time as S100.

Every fault mode that considers up to a predetermined number ofsimultaneous satellite failures is preferably analyzed. However, everypotential fault mode, fault modes with a predetermined probability ofoccurring, a predetermined set of fault modes, and/or any suitable faultmodes can be analyzed. The fault modes to be considered can depend onthe application (e.g., a target protection level, a target positionaccuracy, a location of the external system, etc.), a probability of oneor more satellites and/or satellite constellations failing, and/or canbe otherwise determined.

In an illustrative example, the maximum number of simultaneous satellitefailures to be considered can be determined using: N_(fault_max)=_(r>0)^(argmax)[P_(multiple)(r)<P_(threshold)], where N_(fault_max)corresponds to the maximum number of simultaneous satellite failures,P_(threshold) corresponds to a threshold probability, r is an integer(e.g., number of simultaneous faults to consider such as satellites,observations, etc. such as 1, 2, 3, 4, 5, 7, 10, 15, 20, etc.), and anupper bound on P_(multiple)(r) can be determined according to

${{{P_{multiple}(r)} = \frac{\left( {\sum\limits_{k = 1}^{{Nsat} + {Nconst}}P_{{event},k}} \right)^{r}}{r!}},}\mspace{11mu}$where N_(sat) corresponds to the number of satellites in the set ofsatellites, N_(const) corresponds to the number of satelliteconstellations represented in the set of satellite observations, andP_(event,k) is a predetermined and/or estimated probability of an eventoccurring. Often, N_(fault_max) is between about 1 and 3, inclusive.However, N_(fault_max) can be any value between 1 and the number ofsatellites in view of the receiver (e.g., number of satellites in theset of satellites, number of satellites associated with a satelliteobservation, etc.). The threshold probability can be a predeterminedthreshold (e.g., 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, 10⁻⁹, 10⁻¹⁰,10⁻²-10⁻¹⁰%, etc.), depend on the application, depend on a targetintegrity (e.g., target protection level), depend on the satellites inview (e.g., satellite constellations that are observed, depend on thenumber of satellites in view, etc.), and/or be otherwise determined.

In a first variant of S400, as shown in FIG. 5A, the set of fault modescan be generated or determined by iterating through every possiblecombination of removing satellite observations correspond to at mostN_(fault_max) satellite failures to generate each unique permutation ofsatellite observations excluding m satellites, where m is an integerless than or equal to N_(fault_max). In examples of the first variant,the set of fault modes can exclude fault modes corresponding to faultsin the reference satellite which may lead to larger (or erroneous)protection levels.

In a second variant, the set of fault modes can be generated ordetermined by iterating through every possible combination of removingsatellite observations corresponding to at most N_(fault_max) satelliteobservations to generate each unique permutation of satelliteobservations excluding m satellite observations, where m is an integerless than or equal to N_(fault__max).

In a variation of the first and/or second variant, the satelliteobservations can be modified such that the fault modes do not need toconsider the processed satellite observations (e.g., do not need to useor see double-differenced measurements). For example, each referencesatellite can have a single-differenced ambiguity of 0, wheredouble-differenced satellite observations can be mapped intosingle-differenced satellite observations by “adding” this ambiguity toeach measurement. Additionally or alternatively, the set of satelliteobservations can be processed (e.g., rotated transformed, etc.) to adifferent representation or space. For instance, double differencedsatellite observations can be transformed to single differencedsatellite observations, to double differenced satellite observationsrelative to a different reference satellite, to undifferenced satelliteobservations, to differenced satellite observations relative to anarbitrary constant (e.g., a predetermined constant, a fixed error, anindependently determined value, etc.), and/or otherwise be transformed.In this variation, a single clock state (e.g., per constellation, persatellite, per reference satellite, per process, etc.) is preferablyretained, which could provide an estimate for the reference satelliteambiguity. This variation can be beneficial for decreasing and/orremoving a need to consider correlations between fault modes. However,this variation can otherwise be achieved.

As shown for example in FIG. 5B, determining the set of fault modes caninclude determining a second reference satellite, where the secondreference satellite is preferably distinct from the reference satellite.The second reference satellite is preferably not potentially faulty(e.g., selected from a fault mode or associated set of satelliteobservations that exceeds a threshold probability of not including afault), but can be randomly or pseudorandomly selected (e.g., from theset of satellites excluding the original reference satellite), selectedbased on a property of the second reference satellite (e.g., line ofsight to the satellite, signal strength, predicted availability,predicted duration of the satellite remaining in view of the receiver,etc.), can be potentially faulty (e.g., selected from a fault mode orassociated set of satellite observations that exceeds a thresholdprobability of including a fault), and/or can otherwise be selected. Ingeneral, the second reference satellite is or will be tested for apotential fault (e.g., during a second instance of S400 and/or S500),but the second reference satellite does not have to be tested for apotential fault. The set of fault modes can include a subset of faultmodes corresponding to satellite observations processed relative to thereference satellite and a second subset of fault modes corresponding tosatellite observations processed relative to the second referencesatellite (e.g., by applying a transformation as described above tochange from a first differenced space to a second differenced space).The two subsets of fault modes are preferably distinct (e.g., accountfor different fault modes), but can be overlapping and/or otherwiserelated. In a specific example, the second subset of fault modes can bedetermined by applying a second transformation matrix to the satelliteobservations (e.g., the transformed satellite observations), where thesecond transformation matrix transforms the satellite observations to asecond differenced space (e.g., a fault tolerant second space). Thesecond transformation matrix can be determined by taking theintersection between the transformation matrix (e.g., the transformationmatrix that transforms the satellite observations to the differencedspace) and the matrix formed by dropping the rows corresponding to theobservations assumed to be faulting from the identity matrix. Thisspecific example can have the benefit of including the maximum number ofsatellite observations within the fault mode (e.g., no other set ofsatellite observations can be generated that is fault tolerant such asto test for potential faults in the reference and/or faultingsatellite(s) and has more observations).

Determining a fault parameter S500 preferably functions to determine afault parameter (e.g., for each fault mode) that is correlated withand/or indicative of whether any satellites are likely to have faultedand/or whether any satellites have faulted (e.g., failed). The faultparameter is preferably a probability that the fault-tolerant receiverposition associated with the fault parameter exceeds the protectionlevel (e.g., a likelihood that a fault has occurred), but canadditionally or alternatively be a probability that the fault will occuror has occurred, a probability that the fault-tolerant receiver positionalong a reference axis or coordinate exceeds the protection level (e.g.,the protection level for the same reference axis or coordinate), anidentification of a fault as present, and/or any suitable faultparameter.

Determining a fault parameter is preferably performed by the computingsystem (e.g., a fault observation module of the computing system, of theexternal system, of the GNSS receiver, of a cloud server, etc.), but canbe performed by any component. S500 is preferably performed after S400,but can be performed at the same time as S400. Determining a faultparameter preferably tests (e.g., determines a fault parameter for)every fault mode of the set of fault modes, but can test any subset ofand/or suitable fault modes. In variants, as shown for example in FIG.3, determining a fault parameter can include: determining an all-in-viewreceiver position S530, determining fault tolerant receiver positionS560, determining whether each fault tolerant receiver position iswithin a predetermined threshold of the all-in-view receiver positionS590, and/or any suitable steps.

Determining an all-in-view receiver position S530 functions to determine(e.g., estimate, calculate) a receiver position (and associatedvariance) based on a set of satellite observations (e.g., receivedsatellite observations, processed satellite observations,fault-mitigated satellite observations, etc.). The receiver position canbe determined using a Kalman filter (e.g., recursive Kalman filtering),an extended Kalman filter, a particle filter, MD simulations, aleast-squares solution (e.g., an iterative snapshot least-squaresmethod), and/or using any algorithm.

In a specific example, the weighted least-squares method can be definedas:{circumflex over (x)}=(G ^(T) W ⁽⁰⁾ G)⁻¹ G ^(T) W ⁽⁰⁾ y

Where G is a geometry matrix (e.g., a matrix with dimension [n_(sat)×3],calculated from the actual satellite geometry), W is a weight matrix(e.g., a square matrix with each element corresponding to the reciprocalof the square of a given satellite observations variance, a scaledweight such as based on the probability of a fault in the associatedsatellite(s), etc.), {circumflex over (x)} is the estimated receiverposition (e.g., absolute receiver position, relative receiver positionsuch as relative to a previous receiver position, differenced receiverposition, etc.), and y is the set of satellite observations (e.g.,processed satellite observations, unprocessed satellite observations,differenced satellite observations, etc.). In this specific example, thevariance of the receiver position can be S⁽⁰⁾=(G^(T)W⁽⁰⁾G)⁻¹G^(T)W⁽⁰⁾.

Determining the all-in-view receiver position can optionally includestoring one or more pieces of intermediate data (e.g., W, G, W⁻¹, G⁻¹,Ã⁽⁰⁾=G^(T)W⁽⁰⁾G,

⁽⁰⁾)⁻¹, etc.).

Determining the fault-tolerant receiver position S560 functions todetermine (e.g., estimate, calculate) the receiver position using thesets of satellite observations associated with one or more fault modes.The fault tolerant receiver position can be determined in series and/orin parallel (e.g., for each fault mode, for a subset of fault modes,etc.). The fault-tolerant solutions are preferably determined for eachfault mode. However, any subset of fault modes can be used. In aspecific example, when the likelihood of a fault having occurred (e.g.,the fault parameter) exceeds a threshold, no further fault-tolerantsolutions need to be generated. The fault-tolerant receiver positionsare preferably determined in the same manner as (e.g., using differentsatellite observations) the all-in-view receiver position, but can beperformed in any manner. In a specific example, the weight matrix usedto determine the fault-tolerant receiver position can be

$\begin{matrix}{{W_{i,i} = \left\{ \begin{matrix}{0,\mspace{14mu}{{if}\mspace{14mu}{satellite}\mspace{14mu} i\mspace{14mu}{is}\mspace{14mu}{in}\mspace{14mu}{fault}\mspace{14mu}{mode}\mspace{14mu} k}} \\{W_{i,i}^{(0)},\mspace{14mu}{otherwise}}\end{matrix} \right.}.} & \;\end{matrix}$In a second specific example such as when the satellite observations arecorrelated, the weight matrix for the fault tolerant solution can begenerated by inverting the weight matrix of the all-in-view solution toobtain the satellite observation covariance matrix, removing the rowsand columns corresponding to fault mode k, inverting the resultingmatrix to obtain a weight matrix, resizing the transformed weight matrixto the original size (e.g., by adding zeros corresponding to thefaulting observations), and proceeding with the fault tolerant positioncalculation. However, any weight matrix can be used.

Determining the fault-tolerant receiver position preferably includesdetermining a variance of the difference between each fault-tolerantreceiver position and the all-in-view receiver position. In a specificexample, the variance of the fault tolerant receiver position can bedetermined according to S^((k))=(G^(T)W^((k))G)⁻¹G^(T)W^((k)). In arelated specific example, the variance of the difference can bedetermined according to Σ_(ss)^((k))=(S^((k))−S⁽⁰⁾)C_(acc)(S^((k))−S⁽⁰⁾) where Σ_(ss) ^((k)) is thedifference of the variance between fault-tolerant receiver position kand the all-in-view receiver position and C_(acc) is a modifiedobservation covariance matrix.

In some variants, determining the fault-tolerant receiver position canuse a rank one update (e.g., the Sherman-Morrison formula) to modifystored intermediate data (e.g., stored during determining theall-in-view receiver position), which can increase the speed of thecomputations. In a specific example, Ã^((k)) can be determined accordingto Ã^((k))=Ã⁽⁰⁾−W_(i,i) ⁽⁰⁾g_(i)g_(i) ^(T) where g_(i) is the i^(th)column and/or row of the geometry matrix. In this specific example,

( k ) ) - 1 = ( A ∼ ( 0 ) ) - 1 - ( A ∼ ( 0 ) ) - 1 ⁢ g i ⁢ g i T ⁡ ( A ∼ (0 ) ) - 1 1 + g i T ⁡ ( A ∼ ( 0 ) ) - 1 ⁢ g i .

Determining whether each fault tolerant receiver position is within apredetermined threshold of the all-in-view receiver position S590(and/or determining whether the fault parameter exceeds a threshold)functions to detect whether a fault (e.g., a satellite failure) islikely to have occurred and/or has occurred within the set of satelliteobservations. The probability of fault occurrence can be determinedbased on: probability that the fault-tolerant solution exceeds theprotection level is greater than 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷,10⁻⁸, 10⁻⁹, 10-10, 10⁻²-10⁻¹⁰%, etc.; probability of a fault beingpresent is greater than 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 1%, 5%, 10%,20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, etc.; probability of afault exceeds a total integrity risk; probability of a fault exceeds anallocated integrity risk for a given fault; and/or otherwise determined.Determining whether each fault tolerant receiver position is within apredetermined threshold of the all-in-view receiver position ispreferably performed by a computing system (e.g., a fault detectionmodule of a computing system), but can be performed by any component.Determining whether each fault tolerant receiver position is within apredetermined threshold of the all-in-view receiver position preferablyincludes performing a solution separation test, but can additionally oralternatively include performing a p-test, a ratio test, a chi-squaredtest, and/or any suitable tests.

The solution separation test preferably corresponds to a statisticalassessment of each fault-tolerant receiver position compared to theall-in-view receiver position, but can be otherwise defined. Thesolution separation test is preferably based on a solution separationthreshold, but can be based on an absolute value and/or be otherwisedefined. The solution separation threshold can be determined accordingto an equation, using machine learning, heuristically, empirically,based on an application (e.g., a target protection level, an environmentthat the external system is operating in such as an urban environment vsan open sky environment, etc.), predetermined, and/or otherwisedetermined. The solution separation threshold can be determined based onthe variance of the all-in-view receiver position, the covariance of theall-in-view receiver position, a variance and/or covariance of one ormore fault-tolerant receiver positions, a prior solution separationthreshold (e.g., associated with a previous epoch), an integrity risk, atotal integrity risk, a target integrity, and/or otherwise bedetermined. The solution separation threshold can be a manifold (e.g., asurface, a volume bounded by a surface, a volume excluded from asurface, etc.), a coordinate frame, a vector space, and/or have anysuitable form. For example, the solution separation threshold can definea prismatoid (e.g., rectangular prism), an ellipsoid (e.g., a sphere, aspheroid, a tri-axial ellipsoid, etc.), a homoeoid, a cylinder, acapsule (e.g., sphereocylinder), ovoid, and/or define any suitablevolume or have any suitable shape. Generally, when a solution (e.g., anall-in-view receiver position, a fault-tolerant position, etc.) iswithin the solution separation threshold (e.g., within the surface orvolume defined by the solution separation threshold), the solution isconsidered not likely to be faulty and when the solution is outside ofthe solution separation threshold, the solution is considered likely tobe faulty (e.g., the probability of the solution and/or associatedsatellites or satellite observations being faulty exceeds a threshold).However, solutions can otherwise be considered faulty or not faulty withrespect to the When the difference between each fault-tolerant receiverposition and the all-in-view receiver position is less than the solutionseparation threshold, the fault mode can be accounted for (e.g., in thegoverning equation), the fault-tolerant receiver position can be notlikely faulty (e.g., less than a threshold probability of a faultassociated with a satellite or satellite observation), no fault can bedetected, and/or the fault mode(s) can otherwise be treated. When thedifference between a fault-tolerant receiver position and theall-in-view receiver position is greater than or equal to the solutionseparation threshold, a fault mode can be detected (and subsequentlymitigated and/or identified), the fault mode or associated data can belikely to include a fault, and/or the fault mode can otherwise betreated. However, the solution separation threshold can be otherwiseused.

In an illustrative example, the solution separation threshold, T_(q)^((k)), can be determined according to T_(q) ^((k))=K_(FA,q)σ_(ss,q)^((k)) where

$K_{{FA},q} = {Q^{- 1}\left( \frac{P_{{FA},q}}{2N_{{fault}\_{modes}}} \right)}$and can be used as a scaling factor for scaling the variance (and/orcovariance) of the receiver position. In a related example, the solutionseparation test can correspond to

$\begin{matrix}{{\frac{{{\overset{\hat{}}{x}}_{q}^{(k)} - {\overset{\hat{}}{x}}_{q}^{(0)}}}{T_{q}^{(k)}} \leq}1.} & \;\end{matrix}$

In some embodiments, determining whether each fault tolerant receiverposition is within a predetermined threshold of the all-in-view receiverposition can include correcting for the correlated satelliteobservations, which can function to determine more accurate protectionlevels. Correcting for the correlated observations can includeaccounting for the receiver position covariances (e.g., in addition tothe receiver position variance) in the solution separation test (e.g.,via the solution separation threshold). As shown for example in FIG. 7,when covariances are excluded, the solution separation or tolerancethreshold can define a rectangular space bounded by the threshold ineach geometric direction. When covariances are included, the thresholdregion (e.g., manifold) can become an ellipsoidal region (e.g.,asymmetrical ellipsoid, tilted ellipsoid, etc.). As shown for example inFIG. 7, solutions (e.g., fault-tolerant solutions) that can be indicatedas acceptable (e.g., identified as not faulty or not likely to befaulty) within a rectangular manifold defining the solution separationthreshold (e.g., derived by using all-in-view receiver positionvariances) can be found to have (or be likely to have) a fault when anellipsoidal manifold (e.g., derived by using all-in-view receiverposition variances and covariances) is used to define the solutionseparation threshold. Therefore, accounting for solution covariances canbe beneficial for representing or producing a more conservativeintegrity (e.g., compared to using solution variances). In anillustrative example of these embodiments, the solution separationthreshold can be

${T_{q}^{(k)} = {K_{{FA},q}{\sqrt{\sum\limits_{{ss},q}^{(k)}}.}}}\mspace{11mu}$In related examples, such as where T_(q) ^((k)) can additionally oralternatively be used as an upper bound for the receiver position error,T_(q) ^((k)) can be a maximum value of the ellipsoid (e.g., along eachaxis or coordinate q, the maximum value of the ellipsoid, etc.),determined using principal component analysis, determined using aniterative approach through the ellipsoidal volume, determined usingeigenvalue decomposition, determined using single value decomposition,and/or otherwise be determined.

In some variants, determining whether each fault tolerant receiverposition is within a predetermined threshold of the all-in-view receiverposition can include performing a chi-squared test. The chi-squared testcan be performed in addition to and/or instead the solution separationtest. In these variants, the chi-squared test is preferably an upperbound for all of the solution separation tests. When the chi-square testfails, the resultant receiver position and associated protection levelscan be considered invalid (e.g., even when each solution separation testpasses), one or more mitigation steps can be performed, a new set ofsatellite observations can be received (e.g., the method can berestarted), and/or any suitable response can occur.

Mitigating the effect of faults S600 functions to decrease the effect offault modes and/or satellite observations associated with satellitesand/or satellite constellations that are likely to have (e.g., based onthe fault parameter) and/or have faulted on the position solution andthe associated protection level when faults are likely and/or detected.

S600 is preferably performed when a fault parameter associated withparticular fault mode(s) exceeds a threshold (e.g., a thresholdprobability). However, S600 can be performed independent of the faultparameters, when a predetermined number or type of fault modes aredetected, and/or otherwise be performed responsive to or based on thefault parameters. The threshold can be a predetermined threshold (e.g.,1, 0.1, 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, 10⁻⁹, 10⁻¹⁰,10⁻²-10⁻¹⁰%, etc.), be determined based on the application (e.g., be asmaller threshold for applications that require higher accuracy), bedetermined based on the probability of a particular fault mechanism, bedetermined empirically, be determined heuristically, be determinedaccording to an equation, and/or be otherwise determined. When the faultparameter associated with a particular fault mode is less than or equalto a threshold, the method can accommodate any error introduced by thefault mode (e.g., the receiver position determined including thesatellite observations within the fault mode will not exceed theprotection level). However, any suitable action can occur based on thevalue of the fault parameter relative to the threshold.

When S600 is performed, the resultant fault-mitigated receiver positioncan subsequently be used in lieu of the fault-unmitigated receiverposition (e.g., in S700), or otherwise used.

Mitigating the effect of faults is preferably performed by a computingsystem (e.g., a fault mitigation module of the computing system), butcan be performed by any component. In variants, mitigating the effect offaults can include: removing one or more satellite observations from theset of satellite observations, applying a weight factor to one or moresatellite observations (e.g., apply a smaller weight factor to satelliteobservations associated with a fault mode, apply a larger weight factorto satellite observations not associated with a faulting satellite,etc.), acquiring additional satellite observations, choosing a newreference satellite and transforming the satellite observationsaccording to the new reference satellite, and/or any suitable mitigationsteps. Mitigating the effect of faults is preferably followed bydetermining a fault parameter for the set of satellite observationswhere the faults have been mitigated and/or determining fault modes forthe set of satellite observations where the faults have been mitigated.However, mitigating the effect of faults can be followed by determiningthe GNSS receiver position and associated protection level, processingthe satellite observations, and/or by any suitable step.

In an illustrative example of S600, a fault mode (and/or associated setof satellite observations) that indicates at least a thresholdprobability of a satellite fault (e.g., a fault parameter that is atleast a threshold value) has occurred can be defined as (e.g., used as)a second all-in-view set of satellite observations (e.g., which can beused to repeat step S400 and S500 with the second all-in-view set ofsatellite observations).

Determining the GNSS receiver position and associated protection levelfunctions S700 to estimate (e.g., calculate) a receiver position, aresidual of the receiver position, and/or a protection level of thereceiver position. The receiver position is preferably determined by acomputing system (e.g., a positioning module of a computing system, ofan external system, of a GNSS receiver, of a cloud computing system,etc.), but can be determined by any component.

The receiver position (and residual) can be the position solutiondetermined by the all-in-view receiver position, the position solutiondetermined by a fault-tolerant solution, determined usingambiguity-resolved carrier phase measurements, calculated relative to abaseline between the receiver and a reference station, and/or otherwisebe determined.

The protection level is preferably determined (e.g., calculated,estimated) using a governing equation (e.g., a nonlinear governingequation), but can be determined using machine learning (e.g., a neuralnetwork, decision trees, regression analysis, Bayesian networks, etc.),and/or in any manner. The protection level is preferably a conservativeprotection level (e.g., overestimate of the actual protection leveland/or receiver position, to better ensure that the external system canbe operated according to target operation parameters, etc.), but canproduce an exact and/or aggressive (e.g., underestimate of the actualprotection level). The governing equation can be solved using aniterative approach (e.g., an iterative half-step approach), a binarysearch (e.g., a naïve binary search approach), using an equation (e.g.,a closed-form approximation to the governing solution such as an upperor lower bound to the solution), graphically, using machine learning,and/or in any manner.

In a specific example, the governing equation can be given by:

$\begin{matrix}{{{{2{Q\left( \frac{L_{q} - b_{q}^{(0)}}{\sigma_{q}^{(0)}} \right)}} + {\sum\limits_{k = 1}^{N_{{fault}\_{modes}}}{P_{fault}^{(k)}{Q\left( \frac{L_{q} - T_{q}^{(k)} - b_{q}^{(k)}}{\sigma_{q}^{(k)}} \right)}}}} = {I_{q}\left( {1 - \frac{P_{unmonitored}}{I_{tot}}} \right)}}\mspace{11mu}} & \;\end{matrix}$

Where L_(q) is the protection level associated with axis q (e.g.,horizontal axis, vertical axis), −^((k)) represent quantities associatedwith fault mode k where k=0 refers to the all-in-view receiver position,Q is a tail probability distribution (e.g., of a zero-mean unit Normaldistribution), σ_(q) is a standard deviation of the position solutionalong axis q, P_(fault) is the probability of a given fault (e.g.,determined from a third-party message such as an Integrity SupportMessage, determined heuristically, determined based on previousmeasurements, etc.), T_(q) is the solution separation threshold on axisor coordinate q, b_(q) is the nominal bias contribution (e.g., a worstcase nominal bias contribution) to the position solution on axis orcoordinate q (e.g., approximately 0, b_(q) ^((k))=Σ_(i=1) ^(N) ^(sat)|S_(q,i) ^((k))|b_(nom,i) where S_(q,i) ^((k)) is the fit matrix andb_(nom) is a vector of per-observation biases, etc.), I_(tot) is thetotal integrity budget (e.g., total integrity budget for each axis orcoordinate q, total integrity risk for satellite failures, etc.), andP_(unmonitored) is the probability of unmonitored fault modes.

$Q\left( \frac{L_{q} - T_{q}^{(k)} - b_{q}^{(k)}}{\sigma_{q}^{(k)}} \right)$can, for instance, be the integrity risk (e.g., estimated integrityrisk) for a given fault mode k (and can be independently or withinduring the protection level and/or positioning calculation bedetermined). However, any governing equation can be used.

In a second specific example, which can be particularly but notexclusively beneficial when position covariances are accounted for(e.g., in S500), the governing equation can be given by:

$\begin{matrix}{{{2{Q\left( \frac{L_{q} - b_{q}^{(0)}}{\sigma_{q}^{(0)}} \right)}} + {\sum\limits_{k = 1}^{N_{{fault}\_{modes}}}{P_{fault}^{(k)}{Q\left( \frac{L_{q} - T_{q}^{(k)} - b_{q}^{(k)}}{\sqrt{\sum\limits_{{ss},q}^{(k)}}} \right)}}}} = {I_{q}\left( {1 - \frac{P_{unmonitored}}{I_{tot}}} \right)}} & \;\end{matrix}$

Where

${Q\left( \frac{L_{q} - T_{q}^{(k)} - b_{q}^{(k)}}{\sqrt{\sum\limits_{{ss},q}^{(k)}}} \right)}\mspace{11mu}$can be the integrity risk for fault mode k.

In some variations of these specific examples, T_(q) can be replacedwith a maximum value of the threshold's bound along an axis orcoordinate (e.g., a maximum value of the manifold's bound in eachdirection), a value of the threshold in one axis or coordinate when theother axes coordinates are 0 (e.g., the threshold's value along an axisor coordinate), a value of a bounding box that encloses the threshold,and/or any suitable value can be used.

However, any governing equation can be used.

In some embodiments (e.g., as shown for example in FIGS. 8A and 8B),more than one receiver position can be determined. For instance, areceiver position can be determined according to an ARAIM algorithm anda second receiver position can be determined from a baseline vectordetermined based on the integer ambiguity. When more than one receiverposition is determined, the receiver positions can be used to validatedand/or verify the results (e.g., compared to one another), a preferredvalue can be used (e.g., a receiver position associated with a preferreddetermination method can be used), a receiver position with anassociated integrity can be used, an average (or other combination of)the receiver positions can be used, a receiver position can be selected(e.g., by voting), and/or any suitable receiver position can be used.

In variants, the receiver position and/or protection level can betransmitted to an external system, stored (e.g., cached), used tooperate and/or control an external system, used to generate externalsystem operation instructions (e.g., turn, accelerate, decelerate,acquire data, etc.), and/or used in any manner.

In a specific example as shown in FIG. 6, the method can include: at aGNSS receiver, receiving a set of satellite observations; at a referencestation, receiving a set of satellite observations; transforming thesatellite observations to a set of double differenced satelliteobservations; determining integer carrier phase ambiguities; removingthe integer carrier phase ambiguities from the double differencedsatellite observations; determining a set of fault modes; calculating anall-in-view receiver position; calculating a fault tolerant solution foreach fault mode; when the fault tolerant receiver position(s) pass asolution separation test, estimating the receiver position andprotection levels; when a fault tolerant solution does not pass thesolution separation test, removing the corresponding fault mode from thedouble differenced satellite observations and repeating the calculationof the all-in-view receiver position with the updated double differencedsatellite observations. In variations of this specific example, when thereference satellite that was used to calculate the differenced satelliteobservations for a particular fault mode is itself assumed to fault, anew set of double-differenced satellite observations can be determinedwithout the reference satellite and/or with a different satellite as thereference satellite, and the method repeated using the new set ofdouble-differenced satellite observations. However, the method can beotherwise performed.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),concurrently (e.g., in parallel), or in any other suitable order byand/or using one or more instances of the systems, elements, and/orentities described herein.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for determining a receiver position comprising:receiving a set of satellite carrier phase observations from a set ofsatellites; receiving a set of reference observations from a set ofreference stations; determining double-differenced observations based onthe set of satellite carrier phase observations and the set of referenceobservations; estimating an all-in-view position of the receiver byprocessing the double-differenced observations using a snapshotleast-squares calculation; determining a set of fault modes, where eachfault mode is associated with between one and N potentially faultysatellites, where N is a number of satellites in view of the receiver,wherein each fault mode is associated with a subset of thedouble-differenced observations with the potentially faulty satellitesexcluded; for each fault mode: determining a fault-tolerant position ofthe receiver by processing the double-difference observations in therespective fault mode; comparing the all-in-view position and the faulttolerant position of the receiver; when the all-in-position and thefault tolerant position of the receiver for each fault mode are within asolution separation threshold that depends on the covariances of theall-in-view receiver position, calculating a protection level associatedwith the all-in-view position of the receiver; and when the all-in-viewposition and the fault tolerant position of the receiver for a faultmode differ by more than the solution separation threshold, determininga second set of double-differenced satellite observations that excludesobservations associated with the potentially faulty satellites of thefault mode.
 2. The method of claim 1, wherein the protection level iscalculated based on the covariances of the all-in-view receiverposition.
 3. The method of claim 1, further comprising determining anintegrity risk for each fault mode, wherein the integrity risk isdetermined based on the covariances.
 4. The method of claim 3, whereindetermining the integrity risk comprises bounding a solution separationvector with a maximum bound on the solution separation threshold.
 5. Themethod of claim 1, further comprising determining operation instructionsfor a vehicle based on the all-in-view receiver position, wherein thereceiver is mounted to the vehicle.
 6. A method for determining areceiver position comprising: receiving satellite observations from aset of satellites; receiving reference observations from a set ofreference receivers; determining differenced observations based on thesatellite observations and the reference observations; determining anall-in-view position of the receiver based on the differencedobservations; determining a set of fault modes, each associated with asubset of the differenced observations, wherein each subset of thedifferenced observations excludes differenced observations associatedwith a distinct set of potentially faulty satellites; for a fault modeof the set of fault modes, determining a fault-tolerant position of thereceiver using the subset of differenced observations associated withthe fault mode; and when the all-in-view position and the fault tolerantposition of the receiver for each fault mode are within a solutionseparation threshold, calculating a protection level associated with theall-in-view position of the receiver.
 7. The method of claim 6, furthercomprising, when the all-in-view position and the fault tolerantposition of the receiver differ by more than the solution separationthreshold, determining a second set of differenced satelliteobservations that excludes observations associated with the potentiallyfaulty satellites associated with the fault mode, wherein the receiverposition is determined based on the second set of differenced satelliteobservations.
 8. The method of claim 7, further comprising: when thepotentially faulty satellites comprises a reference satellite,determining the second set of differenced observations using a secondreference satellite.
 9. The method of claim 7, wherein the second set ofdifferenced satellite observations are determined by transforming thesatellite observations using a transformation matrix formed from theintersection of a differencing matrix and an identity matrix thatexcludes satellite observations associated with potentially faultysatellites.
 10. The method of claim 9, wherein the transformation matrixtransforms the satellite observations to a second differencedobservations set that is distinct from the differenced observations. 11.The method of claim 6, wherein the solution separation threshold dependson variances and covariances of the all-in-view receiver position. 12.The method of claim 6, wherein the difference observations comprisedouble-differenced observations.
 13. The method of claim 6, wherein theall-in-view receiver position using a snapshot least-squarescalculation.
 14. The method of claim 6, wherein each set of potentiallyfaulty satellites comprises at most a maximum number of potentiallyfaulty satellites based on a probability that the maximum number ofpotentially faulty satellites have contemporaneous faults.
 15. Themethod of claim 6, wherein the satellite observations consist ofsatellite carrier phase data.
 16. The method of claim 6, wherein thedifference observations are determined based on only satelliteobservations associated with resolved integer ambiguities.
 17. Themethod of claim 16, wherein integer ambiguities are resolved by:determining a set of float phase ambiguities associated with thedifference observations; determining a set of integer phase ambiguityhypotheses using an integer search algorithm; and selecting an integerphase ambiguity of the set of integer phase ambiguity hypotheses basedon results of a hypothesis test comparing integer phase ambiguities ofthe set of integer phase ambiguity hypotheses.
 18. The method of claim6, wherein the satellite observations and the reference observations arereceived during a first epoch, the method further comprising: during asecond epoch, receiving a second set of satellite observations from asecond set of satellites; during the second epoch, receiving a secondset of reference observations from a second set of reference receivers;determining a second all-in-view position of the receiver based on thesecond set of satellite observations and the second set of referenceobservations; determining a second set of fault modes and a secondfault-tolerant position of the receiver associated with a fault mode ofthe second set of fault modes; and when the second all-in-view positionand the second fault tolerant position are within the solutionseparation threshold, calculating a second protection level associatedwith the all-in-view position of the receiver.
 19. The method of claim18, wherein the second set of fault modes is independent of the set offault modes.
 20. The method of claim 6, further comprising determiningoperation instructions for a vehicle based on the all-in-view receiverposition, wherein the receiver is mounted to the vehicle.